F
licenseNot graded
qualityD
maintenanceA Model Context Protocol server that provides current weather information and 3-day forecasts for specified cities using the Open-Meteo API.
1
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MCP Weather Server
by microsoft
Provides real-time weather data for any city through a get-weather tool that allows Copilot to answer questions about current weather conditions in specific locations.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Weather Serverwhat's the weather like in Tokyo right now?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Weather Server
A simple Model Context Protocol (MCP) server that provides real-time weather data to AI agents like GitHub Copilot.
Quick Start
Clone the repository:
git clone https://github.com/microsoft/lets-learn-mcp-javascript.git
cd mcp-weather-server1. Install Dependencies
npm install2. Run the Server
Test with MCP Inspector:
npx -y @modelcontextprotocol/inspector npx -y tsx main.ts3. Use with VS Code
Open the
mcp.jsonfile in.vscodefolderClick the start server button above line 4
Open Chat mode and select agent and choose a modal that supports MCPs such as Claude Sonnet
Type or speak into the chat and ask it what the weather is like in your city
Related MCP server: MCP Weather Server
Features
๐ค๏ธ Real-time weather data for any city
๐ No API key required (uses Open-Meteo)
๐ค Works with GitHub Copilot and other MCP-compatible AI tools
โก Easy to test with MCP Inspector
Usage Examples
Ask GitHub Copilot:
"What's the weather like in Tokyo?"
"How's the weather in London today?"
"Give me the current weather for Paris"
How It Works
The server provides a get-weather tool that:
Converts city names to coordinates using geocoding
Fetches current weather data from Open-Meteo API
Returns structured data that AI agents can format beautifully
Code Structure
// Creates MCP server with weather tool
const server = new McpServer({
name: "Weather Server",
version: "1.0.0"
});
// Defines the get-weather tool
server.tool('get-weather', 'Tool to get the weather of a city', ...);
// Connects via stdio transport
const transport = new StdioServerTransport();
server.connect(transport);Dependencies
@modelcontextprotocol/sdk- MCP server frameworkzod- Schema validationtsx- TypeScript execution (for development)
API Used
Open-Meteo - Free weather API with no authentication required
Available Tools
2 toolsget_alertsB
Get weather alerts for a state
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Two-letter state code (e.g. CA, NY) |
TDQS
B3.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It does not mention that the tool is read-only, any rate limits, or what the output contains. The description is too minimal to inform an agent about side effects or safety.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no unnecessary words. It is efficiently structured and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, the description is incomplete. It does not describe the return format or structure of alerts, which is important for an agent to process the results. For a simple tool, this gap is significant.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the schema already documents the 'state' parameter fully (two-letter code, example). The description adds no additional information about parameters, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'weather alerts' with a qualifier 'for a state', making the purpose unambiguous. The sibling tool 'get_forecast' is distinct enough to differentiate without explicit mention.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'get_forecast'. No mention of prerequisites or conditions for use. The description only states what the tool does, not when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_forecastB
Get weather forecast for a location
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude of the location | |
| longitude | Yes | Longitude of the location |
TDQS
B3.1/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits, but it only states 'Get weather forecast for a location'. It does not mention what kind of forecast (current, daily, hourly), potential destructive actions (none implied but not confirmed), or any required permissions. The agent has no insight into side effects or return behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It is appropriately concise for a simple tool with two well-documented parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 params, no output schema, no annotations), the description is minimally adequate. However, it omits key details such as the forecast type, time horizon, units, and any specifics about the response. A slightly more complete description would improve usability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with each parameter having a clear description. The tool description adds no further parameter meaning. Baseline score of 3 is appropriate since the schema already documents parameters adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (get) and resource (weather forecast) with a location qualifier. However, it does not differentiate from the sibling tool 'get_alerts', which likely also relates to weather. Without distinguishing context, the agent may not know when to choose one over the other.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus the alternative 'get_alerts'. There is no mention of prerequisites, typical use cases, or selection criteria, leaving the agent without context to make an informed choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
- First observed
get_alerts - First observed
get_forecast
TDQS
B3.1/5.0
Scored across 2 tools
Disambiguation5/5
The two tools have clearly distinct purposes: get_alerts retrieves weather alerts for a state, while get_forecast provides weather forecasts for a location. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the need for alerts versus forecasts.
Naming Consistency5/5
Both tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive nouns (alerts, forecast). This uniformity enhances readability and predictability, making the tool set easy to navigate and understand.
Tool Count2/5
With only 2 tools, the server feels under-scoped for a weather domain, which typically involves more operations like current conditions, historical data, or radar information. This limited set may restrict agent capabilities in handling comprehensive weather-related tasks.
Completeness2/5
The tool surface is significantly incomplete for a weather server, lacking essential operations such as getting current conditions, historical weather data, or radar imagery. This creates notable gaps that could lead to agent failures when trying to perform common weather-related workflows.
Maintenance
ActivityInactive
ResponsivenessNo issues
Related MCP Connectors
The official Model Context Protocol server for Ambee. It gives any MCP-compatible AI assistant โ Claude, ChatGPT, Cursor, VS Code, Ollama, and more direct access to live air quality, pollen, and weather data. To get started, including information on signing up and obtaining your Ambee key, check out the Ambee documentation on https://docs.ambeedata.com
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yoโฆ
An MCP server for weather information by @kulybaba
An MCP server for weather information by @kulybaba
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